Click-through-rates are measures of online usage used for various industries. To compute a click-through-rate, historical patterns of online usage from users, including usage involving selecting (also known as clicking) objects in a web browser such as links or advertisements, are examined. The click-through-rate is essentially a count of a number of clicks over some other variable (typically number of impressions, or time). One common use for click-through-rates is in ranking search results responsive to search queries. When a user performs an online search, a number of potentially matching documents can be returned and search engines or other services often will rank these results according to various metrics, including prevalence of keywords in the results and how often the result is linked by other web sites (PageRank) as well as metrics such as click-through-rate, where results that have been frequently clicked on may be ranked higher than comparable results that have not been frequently clicked on. While for the most part calculations of click-through-rates are fairly straightforward, there are certain areas where the calculations become more tricky. One such area is in measuring click-through-rates for objects that have a limited lifespan, and thus there may not be enough, or even any, historical usage data to make useful conclusions about those limited-lifespan objects. One such limited lifespan object is a job posting.
In recent years it has become more and more prevalent for job hunters to utilize the Internet to perform their job search, typically by performing searches on job listings posted online by hiring companies and/or recruiters. Because job postings are usually only posted for a limited time (e.g., until the job opening is filled), it can be difficult to obtain historical usage information for individual job postings. In light of this, the technical calculations used to rank job postings responsive to a search may be inaccurate because they are based on click-through-rates of job listings with little or no historical usage information.